{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parallel analysis of MERRA-2 and Alpine-3D grids using `xarray` and `dask`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import xarray as xr\n",
    "from dask.distributed import Client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create dask cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"border: 2px solid white;\">\n",
       "<tr>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Client</h3>\n",
       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
       "  <li><b>Scheduler: </b>tcp://127.0.0.1:37704</li>\n",
       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a></li>\n",
       "</ul>\n",
       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
       "  <li><b>Workers: </b>12</li>\n",
       "  <li><b>Cores: </b>24</li>\n",
       "  <li><b>Memory: </b>60.00 GB</li>\n",
       "</ul>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Client: 'tcp://127.0.0.1:37704' processes=12 threads=24, memory=60.00 GB>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client = Client(n_workers=12, threads_per_worker=2, memory_limit='5GB')\n",
    "client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "nc_path = \"/scratch/summit/erke2265/MERRA2/T2M_hourly_*.nc\"\n",
    "var=\"T2M\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Caclulate and plot temporal mean using dask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 578 ms, sys: 108 ms, total: 686 ms\n",
      "Wall time: 3.34 s\n"
     ]
    },
    {
     "data": {
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       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 81.60 GB </td> <td> 232.70 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (350640, 101, 576) </td> <td> (1000, 101, 576) </td></tr>\n",
       "    <tr><th> Count </th><td> 760 Tasks </td><td> 360 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"80.588235,70.588235 106.000852,70.588235 106.000852,96.000852 80.588235,96.000852\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"93.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >576</text>\n",
       "  <text x=\"126.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,83.294544)\">101</text>\n",
       "  <text x=\"35.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,80.706734)\">350640</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "dask.array<concatenate, shape=(350640, 101, 576), dtype=float32, chunksize=(1000, 101, 576), chunktype=numpy.ndarray>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time \n",
    "# Define chunk size and insepct dask array\n",
    "ds_dask = xr.open_mfdataset(nc_path, chunks={'time': 1000}, combine='by_coords')\n",
    "ds_dask[var].data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.28 s, sys: 580 ms, total: 4.86 s\n",
      "Wall time: 23.5 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2b416b0baa90>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "# Compute temporal mean and plot\n",
    "ds_dask_mean = ds_dask[var].mean(dim='time')\n",
    "ds_dask_mean.plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "alpine3d",
   "language": "python",
   "name": "alpine3d"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
